Nalan ǑzkurtN. Herencsar2025-10-062018978153864695310.1109/TSP.2018.8441404https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053509978&doi=10.1109%2FTSP.2018.8441404&partnerID=40&md5=a325ef0e8a6dbac40b399b9cd79ccc65https://gcris.yasar.edu.tr/handle/123456789/9535Tunable Q wavelet transform (TQWT) was recently proposed as an efficient wavelet decomposition method which can match to the oscillatory behaviour of the signal. The selection of Q-factor is an important issue in obtaining a sparser signal representation by TQWT. Morphological component analysis (MCA) is a signal separation method which uses the tuning property of TQWT by selecting a low and a high Q-factor matches the signal components. However the Q-factors are usually chosen experimentally or using the prior information. Thus in this study a signal adaptive Q-factor selection method which can be used with TQWT based analysis was proposed. The performance of the proposed algorithm is illustrated with two examples using MCA signal separation. © 2018 Elsevier B.V. All rights reserved.EnglishMorphological Component Analysis, Tunable-q Wavelet Transform, Wavelet Energy-entropy Ratio, Factor Analysis, Separation, Signal Processing, Wavelet Decomposition, Morphological Component Analysis, Morphological Component Analysis (mca), Prior Information, Signal Components, Signal Representations, Signal Separation, Tuning Properties, Wavelet Energy, Q Factor MeasurementFactor analysis, Separation, Signal processing, Wavelet decomposition, Morphological component analysis, Morphological component analysis (MCA), Prior information, Signal components, Signal representations, Signal separation, Tuning properties, Wavelet energy, Q factor measurementConference Object